Environments for Automatic Curriculum Learning: A Short Survey

Abstract Reinforcement learning encompasses various approaches that involve training an agent on multiple tasks. These approaches include training a general agent capable of executing a wide range of tasks and training a specialized agent focused on mastering a specific skill. Curriculum learning strategically orders tasks to optimize the learning process, enhancing training efficiency and improving overall performance. Researchers developing novel methods must select appropriate environments for evaluation and comparison with other methods. We introduce an overview of environments suitable for assessing curriculum learning methods, highlighting their key differences. This work details task components, modifications, and a classification of existing curriculum learning methods. We aim to provide researchers with valuable insights into the selection and utilization of environments for evaluating curriculum learning approaches.